Open Reality: phone video to AI-queryable 3D scenes. MCP tools for Claude/Codex/Cursor (npm openreality-mcp), the self-hostable broker, and the VGGT-SLAM library, in one repo. BSD-2-Clause.
# Add to your Claude Code skills
git clone https://github.com/reality-opened/openrealityGuides for using mcp servers skills like openreality.
Last scanned: 9/10/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-09-10T08:41:40.043Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how openreality compares with popular alternatives.
openreality is an open-source mcp servers skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by reality-opened. Open Reality: phone video to AI-queryable 3D scenes. MCP tools for Claude/Codex/Cursor (npm openreality-mcp), the self-hostable broker, and the VGGT-SLAM library, in one repo. BSD-2-Clause. It has 258 GitHub stars.
Yes. openreality passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/reality-opened/openreality" and add it to your Claude Code skills directory (see the Installation section above).
openreality is primarily written in Python. It is open-source under reality-opened on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other MCP Servers skills you can browse and compare side by side. Open the MCP Servers category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh openreality against similar tools.
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| 🎥 Video in, 3D scene out | Upload a phone video. A few minutes later you have a persistent 3D scene. |
| 📏 Measurement | Distances and angles between any points. Numbers are only called metres after you calibrate with real distance; otherwise relative. |
| 🧭 Path planning | Plan a route through the scanned free space to an object or a point. |
| 🤖 Robot-training exports | Turns a scan into LeRobot / GR00T style datasets or an Isaac Sim scene (hosted service). |
| 🕵️ Scene agents | Server-side agents that survey, label, and answer questions about a scene. |
| 🛠️ 41 tools for your AI | Everything is exposed through MCP. |
| 🧪 Offline simulator | A mock backend fakes the entire workflow with fixture data, so you can develop and demo with no account and no GPU. |
| 🏠 Self-hostable | The full server runs on your own GPU box or your own Modal account, no account with us needed. |
Add the tools to your AI assistant:
claude mcp add openreality -- npx -y openreality-mcp serve
codex mcp add openreality -- npx -y openreality-mcp serve
Add this to claude_desktop_config.json (Settings → Developer → Edit Config)
or ~/.cursor/mcp.json:
{
"mcpServers": {
"openreality": {
"command": "npx",
"args": ["-y", "openreality-mcp", "serve"]
}
}
}
Claude Code plugin (the MCP server plus the workflow skill in one step):
/plugin marketplace add reality-opened/openreality
/plugin install openreality@openreality
Claude desktop extension: download openreality-mcp-<version>.mcpb from the
latest release
and double-click it, or drag it into the Claude desktop window. No terminal needed.
Then sign in once (opens your browser, stores a revocable API key on your machine):
npx -y openreality-mcp login
Scan a room with your phone at open-reality.io, or just ask your assistant to upload a video file. Full per-client setup: open-reality.io/mcp.
Once connected, talk to your assistant like this:
"Upload
~/Videos/kitchen.mp4and reconstruct it."
"What objects are in my latest scan, and how big is the room?"
"The counter edge to the window is 2.4 m. Calibrate the scene, then measure the couch."
"Plan a path from the door to the desk and describe it."
"Export this scan as robot-training data and save the zip locally."
The whole workflow is self-hostable. Read
server/docs/self-hosting.md; the short version:
git clone https://github.com/reality-opened/openreality
cd openreality/server
# Path A: your own GPU box (one process, local disk)
python -m server.selfhost --data-dir ~/openreality-data
# Path B: your own Modal account (CPU web server + GPU worker)
modal deploy modal_selfhost.py
Self-hosted servers need no account: a single token printed at first start is
your login, and the MCP client connects with OPENREALITY_URL plus that token.
[!IMPORTANT] Licensing. This repo is BSD-2-Clause, but the 3D reconstruction model a self-hosted server downloads (VGGT-1B and the VGGT code it runs on) is licensed by its owners as CC BY-NC 4.0, non-commercial use only. Nothing here redistributes it; your server fetches it from the source under their terms. For commercial use, use the hosted service (which runs a commercially licensed model) or get your own license from the model owners.
| Directory | What it is | Ships as |
|---|---|---|
mcp/ |
The MCP server: 41 tools, scene resources, the offline simulator, and a full test suite. Developed here directly. | npm openreality-mcp |
server/ |
The backend: turns videos into persistent scenes and serves measurement, planning, agents, and exports over a plain REST API. | source (public mirror) |
core/ |
The 3D reconstruction library: camera tracking and dense geometry from ordinary video (the VGGT-SLAM 2.0 line), plus metric calibration, object detection, and splat export. | source (public mirror) |
server/ and core/ are curated mirrors of our private working repos, synced
by hand; each carries a MIRROR.md that says exactly what is included and how
it is synced. mcp/ is developed in this repo directly.
flowchart LR
A["📱 phone video"] --> B["server: 3D reconstruction<br/>(GPU job)"]
B --> C[("persistent scene:<br/>geometry + objects + report")]
C --> D["41 MCP tools"]
D --> E["🤖 Claude / Codex / Cursor"]
E -->|"measure · navigate · export"| D
The MCP process always runs on your machine and holds your credentials; every tool call is a typed REST call to a server (ours or yours). Big files are written to your disk, never pasted into the AI's context. Server refusals and uncertainty labels reach the AI unedited, so it cannot pretend a relative number is metres.
The measurement rule is explained in Why our 3D tools refuse to say "metres" until you prove it.
Open Reality stands on other people's research and code. Thank you to:
core/ is the VGGT-SLAM 2.0 line, and the scan demo above is theirs.